CREMA-D
PulseAugur coverage of CREMA-D — every cluster mentioning CREMA-D across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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Concept bottleneck models enhance explainability in speech emotion recognition
Researchers have adapted concept bottleneck models, previously used for image classification, to speech emotion recognition (SER). This adaptation aims to improve the explainability of SER systems, particularly those ut…
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ModalShare optimizes bandwidth for multimodal split learning
Researchers have developed ModalShare, a novel system for optimizing bandwidth allocation in multimodal split learning. This approach dynamically assigns bandwidth to different modalities based on their contribution to …
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New GUIDE framework controls multimodal model evidence usage
Researchers have introduced GUIDE, a novel framework designed to control how large multimodal models utilize internal evidence when following language instructions. Unlike previous models that might rely on superficial …
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Audio model research details compute-performance tradeoffs
A new research paper explores the optimal allocation of computational resources for audio models, focusing on Automatic Speech Recognition (ASR) and Speech Emotion Recognition (SER). The study introduces a framework tha…
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New AMRD method enables lightweight speech emotion recognition models
Researchers have developed Adaptive Multi-teacher Relational Distillation (AMRD), a novel method to create lightweight speech emotion recognition (SER) models suitable for on-device applications. AMRD addresses challeng…
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New deep learning models enhance speech emotion recognition accuracy and explainability
Researchers have developed new deep learning techniques for speech emotion recognition (SER), a field crucial for advancing human-computer interaction. One study introduces a hybrid DCRF-BiLSTM model that achieves high …
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New MARS framework tackles incomplete multimodal learning with residual guidance
Researchers have developed MARS (Missingness-Aware Residual-guided Specialization), a novel framework for incomplete multimodal learning. This approach addresses the challenge of missing data modalities during inference…
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Upper-face cues enhance audiovisual sentence recognition under noise
Researchers have explored the impact of upper-face affective cues on audiovisual sentence recognition, particularly when audio quality is degraded. Their study utilized the CREMA-D corpus to train classifiers under vari…
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AI framework uses mixed precision for on-device bipolar agitation detection
Researchers have developed a new framework called MP-IB for disentangling stable speaker traits from volatile affective states in voice data, specifically for detecting bipolar disorder agitation on resource-constrained…